Knowledge base systems include numerous approaches to knowledge representation and manipulation including: ruled based systems, which capture knowledge in the form of structured if-then statements, model based reasoning using software models to capture knowledge or to emulate real processes; neural networks comprising a network of nodes and connections (i.e., neural nets) for capturing knowledge, i.e., the neural nets can learn by using examples; thus, neural networks can be considered a type of artificial intelligence technology that imitates the way a human brain works; fuzzy logic for representing and manipulating knowledge which is incomplete or imprecise; and decision tree implementations which capture decision making that can be expressed as sets of ordered decisions. Fuzzy logic is sometimes combined with other knowledge based technologies.
Artificial intelligence and cognitive modeling involve simulating properties of neural networks, where artificial intelligence technologies solve particular tasks and where cognitive modeling includes building mathematical models of neural systems. Thus, cognitive modeling includes physical and/or mathematical modeling of neural systems behavior.
Generally, cognitive maps are graphical models and/or graphical representations of perceived cause-and-effect (i.e., causal assertions, showing feedback loops) of relationships between concepts, events, and/or actions expressed as directed edges between nodes as illustrated in FIG. 5, which illustrates the cultural belief that working hard will lead to success. These cognitive maps in the form of graphical models differ from other graphical representations for problem solving, such as Bayesian belief nets and influence diagrams, because feedback loops, i.e., cycles, are possible in cognitive maps, where such cycles are not possible in Bayesian belief nets and influence diagrams. Cognitive maps are suited to represent complex models of interactions that evolve with time. Positive or negative causality are specified on the edges to indicate whether an increased strength in a causal node effects an increased or decreased strength in a related node. Fuzzy cognitive maps further expand this representation by assigning a value to the edges in the fuzzy causal range [−1,1] and a value to the nodes in the fuzzy range [0,1].
In some learning paradigms, learning algorithms may fall within the realm of estimation problems, such as statistical modeling, compression, filtering, blind source separation and clustering. There are numerous algorithms available for training neural network models and these algorithms can be characterized as optimization and statistical estimation algorithms. A statistical estimator can be an arbitrary neural model and/or a neural network and/or a neuro-fuzzy model trained by known training methods and pruning methods. In exemplary embodiments, a novel learning algorithm is implemented to train a neural network as a statistical estimator to determine activity parameters.
A Particle Swarm Optimization (PSO) algorithm, based on a cultural learning metaphor, where an agent, represented as an n-dimensional feature vector, adapts its solution in a problem space from its social interactions. A search through the problem space is controlled by an n-dimensional velocity vector giving the learning agent a particle movement characteristic. Two types of interactions are usually distinguished: (1) a top-down type of interaction based on normative knowledge of a global best (also referred to herein as “gbest”) and (2) a bottom-up type of interaction based on internal and neighborhood knowledge of a local best (also referred to herein as “lbest”). Additionally, lbest acts as the agent's episodic memory of past performances. The cognitive and social influences are modulated by the stochastic parameters φ1 and φ2 (lower case Phi 1 and Phi 2), respectively. An inertia parameter omega (ω), decreasing with time, acts as the momentum in the neural networks in controlling the exploration-exploitation tradeoff of the search.
A PSO algorithm implements a stochastic search algorithm similar to genetic algorithms but with a social cognitive metaphor. A PSO algorithm emphasizes reinforcement through imitation instead of reproduction. PSO algorithm particle representation is in real number points in the search space. In a basic PSO algorithm, each particle has a magnitude x and a velocity v; where pl and pg are the local best (lbest) and global best (gbest) for a given particle; where c1 and c2 are the cognitive and social parameters; where r1 and r2 are random numbers [0,1] and at each time step:v(t+1)=v(t)+c1r1(pl(t)−x(t))+c2r2(pg(t)−x(t))  formula (1); andx(t+1)=x(t)+v(t+1)  formula (2).
The velocities (v) are bounded to a value±vmax to control the search. As agents interact, subsets of the population become more similar and successful together or more dissimilar.
According to exemplary embodiments herein, practical applications in psychological warfare tactics, in regard to modeling interactions of agents/actors from different cultures and predicting the viability of war time alliance formation and predicting non alliance formation with groups having different cultures are described. For example, the impact of a foreign presence in a multiethnic society can be modeled, using adaptive algorithms for coalition of cognitive agents.
Therefore, the need exists for a computer implemented method and system of predicting alliance formation among groups, by determining beliefs of a plurality of agents and/or partners.
Further, the need exists for a computer implemented method and system of predicting alliance formation by inferencing a plurality of fuzzy cognitive maps on a linear combination of a plurality of cognitive maps among groups of agents and/or partners.
The need exists for a cognitive map, where feedback loops, i.e., cycles are possible.
The need exists for a cognitive map of an agent that consists of concept nodes and/or utility nodes representing the desirable and/or undesirable goal states, and policy nodes that represent the possible actions of the agent.
The need exists for a computer implemented method and system of predicting alliance formation by adapting a belief system represented by the linear combination of cognitive maps, in response to a social environment represented by a belief system among groups of people.
Furthermore, the need exists for a computer implemented method and system for predicting alliance formation by forming teams and/or coalitions based on inferencing and modifying beliefs, by learning mental properties through experience over time, to achieve mutual beliefs among groups of people.